Paper Info
Title
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics
Abstract
Frequent subgraph mining has been extensively studied on certain graph data. However, uncertainties are inherently accompanied with graph data in practice, and there is very few work on mining uncertain graph data. This paper investigates frequent subgraph mining on uncertain graphs under probabilistic semantics. Specifically, a measure called φ-frequent probability is introduced to evaluate the degree of recurrence of subgraphs. Given a set of uncertain graphs and two numbers 0 S with probability at least (1 - δ/2)s, where s is the number of edges of S. In addition, it is thoroughly discussed how to set δ to guarantee the overall approximation quality of the algorithm. The extensive experiments on real uncertain graph data verify that the algorithm is efficient and that the mining results have very high quality.
Year
DOI
Venue
2010
10.1145/1835804.1835885
KDD
Keywords
Field
DocType
certain graph data,uncertain graph databases,uncertain graph data,mining result,frequent subgraphs,frequent subgraph mining,high quality,probabilistic semantics,real uncertain graph data,graph data,overall approximation quality,frequent probability,uncertain graph
Data mining,Forbidden graph characterization,Computer science,Induced subgraph isomorphism problem,Distance-hereditary graph,Cograph,Factor-critical graph,Universal graph,Subgraph isomorphism problem,Graph (abstract data type)
Conference
Citations 
PageRank 
References 
66
1.69
36
Authors
3
Name
Order
Citations
PageRank
Zhaonian Zou133115.78
Hong Gao21086120.07
Jianzhong Li33196304.46